{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b84daadf-56e7-470f-8bde-69f0414b9f64",
   "metadata": {},
   "source": [
    "```\n",
    "\n",
    "\n",
    "\n",
    "```\n",
    "# A/B测试应用实战（python详解）"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "801536af-8bd8-4b29-9df7-1ec0beb1e615",
   "metadata": {},
   "source": [
    "## 1. 背景\n",
    "&emsp;&emsp;某电商公司非常注重自己的落地页设计，希望通过改进设计来提高转化率。以往该公司全年转化率平均在13%左右，现在希望设计的新页面能够带来更高的转化率，希望新页面的转化率能有2%的提升，达到15%。在正式推出新页面之前，该公司希望通过AB测试在小范围的用户中进行测试，以确保新页面的效果能够达到预期目标。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79fbb104-ac6b-45e1-859b-83c638344444",
   "metadata": {},
   "source": [
    "## 2. A/B测试基本流程"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7d202d2-fa4b-4208-a242-93457b2d31b4",
   "metadata": {},
   "source": [
    "&emsp;&emsp;了解了基本的背景之后，就可以进入AB测试了。在进行AB测试之前，需要梳理一下AB测试的基本流程，以确保实验的顺利进行。AB测试的基本流程包括以下几个步骤：\n",
    "- <font size=3 color=\"orange\">**确定实验目标及衡量指标**</font><br>在本案例中实验目标是通过AB测试确定新落地页是否可以提升2%的转化率。衡量指标即为**页面转化率**<br><br>\n",
    "- <font size=3 color=\"orange\">**设计实验方案**</font><br>设计实验的具体方案，包括实验变量（即页面的具体改动点）、实验时间、实验样本量、划分实验组和对照组等。还需要确定实验数据的收集方法和分析方式。<br>【注】一般分析方式包括描述性统计、假设检验、置信区间估计、回归分析等等。在进行AB测试的时候，需要分析师根据实验的目标和具体情况选择合适的分析方法，并结合实际情况进行解读，提出合理的建议和决策。<br><br>\n",
    "- <font size=3 color=\"orange\">**执行实验并收集数据**</font><br>按照实验方案进行实验执行，同事记录实验数据。<br><br>\n",
    "- <font size=3 color=\"orange\">**数据分析和结果评估**</font><br>对实验数据进行分析和评估，包括对实验结果的显著性检验、效果评估等。最后给出结论和建议。<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e284a8e-ed9f-45b2-b5e8-5eb59497d724",
   "metadata": {},
   "source": [
    "## 3. 设计A/B test实验"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04612394-d616-4c68-a44b-96879fc68560",
   "metadata": {},
   "source": [
    "在A/B测试实验设计这一步，通常需要完成以下6个步骤：\n",
    "- 1、提出假设\n",
    "- 2、确定实验分组\n",
    "- 3、计算实验样本量及试验周期\n",
    "- 4、上线AB测试并收集数据\n",
    "- 5、数据分析及假设检验\n",
    "- 6、得出结论及建议"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "807226ee-4581-47b5-88ec-aef0af6852ec",
   "metadata": {},
   "source": [
    "### **Step1：提出假设**\n",
    "&emsp;&emsp;设计AB test实验的第一步通常是提出假设。假设是对于某个特定变化我们所期望的结果，也是后续实验的基础，我们需要在后续实验中通过数据验证这个假设是否成立。如果验证成立，我们可以将这个变化推广到全部用户。如果验证不成立，则需要继续优化这个假设或者放弃这个修改方案，以寻找更好的变化。<br>\n",
    "&emsp;&emsp;在这个实验中，我们希望新页面可以提升2%的转化率，原则上我们应选择单尾检验，准确的说，**应该选择右侧单尾检验，因为我们的假设是新页面的转化率要大于旧页面的转化率**。但是，在本案例中，我们并不能确定新页面的性能一定比当前的页面更好。所以，这里选择双尾检验。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71bed083-9df3-420a-97ea-9d3337db7966",
   "metadata": {},
   "source": [
    "> <font color=\"grey\">【补充说明】</font><br><br>\n",
    "> <font color=\"grey\">单尾检验和双尾检验是假设检验中的两种常见常见形式。它们的区别在于对假设的方向性不同：\n",
    "> - <font color=\"grey\">在单尾检验中，研究者针对假设提出了明确的方向性，例如“新设计的落地页转化率比原设计高”、“将按钮颜色从灰色改为红色将导致更多的点击”……因此只检验这个方向是否具有统计显著性，被称为单侧检验。</font>\n",
    "> - <font color=\"grey\">而在双尾检验中，研究者不确定假设所在的方向，例如“新设计的落地页转化率与原设计是否存在差异”、“改变页面布局会影响用户的满意度”……需要同时检验两个方向的统计显著性，被称为双侧检验。</font><br><br>\n",
    "> <font color=\"grey\">在AB测试中，一般应根据实验目的、数据分布情况和统计分析方法等因素来确定单尾检验或双尾检验。例如，如果实验的目的是验证新设计的落地页转化率是否明显高于旧设计，就可以选择单尾检验。但如果不确定两种设计是否存在差异，则应选择双尾检验。</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29cc54fd-8434-437a-8b21-6f025fd06d6a",
   "metadata": {},
   "source": [
    "简单来说，可以这样辨别单尾/双尾检验：\n",
    "- <font color=\"orange\">如果备择假设H1中是“≠”，则是双尾检验；</font>\n",
    "- <font color=\"orange\">如果备择假设H1中是“＞”或者“≥”，则是右侧单尾检验；</font>\n",
    "- <font color=\"orange\">如果备泽假设H1中是“＜”或者“≤”，则是左侧单尾检验。</font>\n",
    "\n",
    "此外，需要特别注意的是：**想支持的结论通常放在备择假设上**。所以在本案例中，原假设和备择假设分别是：\n",
    "- **原假设H0：$P=P0$**\n",
    "- **备择假设H1：$P≠P0$**<br><br>\n",
    "其中，$P0$ 代表的是旧版落地页的转化率，$P$ 代表的是新版落地页的转化率。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da6f96cd-a99b-4367-bcdc-7f92ba5d745d",
   "metadata": {},
   "source": [
    "============================="
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ba448e5-c3b0-4824-9559-491edfb6af27",
   "metadata": {},
   "source": [
    "### **Step2：确定实验分组**\n",
    "在此次AB测试中，我们分为实验组和对照组两组：\n",
    "- 对照组（control组）：这一组用户将看到旧版落地页\n",
    "- 实验组（treatment组）：这一组用户将看到新版落地页"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5460eb3-f81c-466c-9578-3b4f20a77559",
   "metadata": {},
   "source": [
    "为了后续计算每一组的转化效率，需要记录每一位参与实验的用户的购买行为，也就是说无论用户看到的是新版落地页还是旧版落地页，都需要记录这位用户最终是否购买了产品。这可以通过在网站上添加相应的追踪代码来实现：\n",
    "- $0$：代表用户在测试期间没有购买产品\n",
    "- $1$：代表用户在测试期间购买了产品\n",
    "\n",
    "这样，后续就可以很容易地计算出每个组的均值，从而得到新旧两版落地页的转化率。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d1d83ab-45f3-408c-a9e9-ba692065d169",
   "metadata": {},
   "source": [
    "============================="
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f241f02f-b5ff-4f40-8f50-a603a5e386b6",
   "metadata": {},
   "source": [
    "### **Step3：计算实验样本量、试验周期**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d51657e-31c8-4820-8f63-fa810cad8b03",
   "metadata": {},
   "source": [
    "- **实验样本量的确定**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53401523-9ae0-42fe-80a0-2feb02574754",
   "metadata": {},
   "source": [
    "我们之前讲过，根据大数定律和中心极限定理，样本量越大我们的估计就会越精确，但同时也意味着成本越高，所以需要知道实验所需的最小样本量是多少，在成本可控范围里，选择合适的样本量即可。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd2225c0-b869-45b0-a77d-691e04f1359e",
   "metadata": {},
   "source": [
    "每一个实验组所需样本量计算公式如下："
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ea7d1d1-8f7b-48c4-90b4-f80cd18d6543",
   "metadata": {},
   "source": [
    "$$ N = \\frac{\\sigma^2}{\\delta^2}(Z_{1-\\frac{\\alpha}{2}}+Z_{1-\\beta})^2$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2ca0929-d9d2-4ff5-8e9e-410014c2467b",
   "metadata": {},
   "source": [
    "在这个公式当中，$\\alpha$为犯第一类错误的概率，$\\beta$为犯第二类错误的概率，σ代表的是样本数据的标准差，δ代表的是预期实验组和对照组两组数据的差值。一般情况下，我们会设置：<br>\n",
    "- 显著性水平：$\\alpha=0.05$，即在拒绝原假设之前，我们有95%的把握新版落地页的转化率比旧版落地页要高\n",
    "- 统计功效（$1-\\beta$）：$\\beta=0.2$，即表示测试检测特定效果的能力，如果该特定效果存在的话。在此案例中就是，如果新版落地页真的比旧版转换率要高，该测试有80%的概率能检测出这个状况。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3d681ee-0772-4727-8282-b593ee85dcec",
   "metadata": {},
   "source": [
    "当衡量指标为比率类指标时，标准差计算公式为："
   ]
  },
  {
   "cell_type": "markdown",
   "id": "764b0792-e0f0-4fb9-b080-9baaa0e41759",
   "metadata": {},
   "source": [
    "$$ \\sigma^2 = P_A(1-P_A)+P_B(1-P_B)$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b4d6c26-66ff-436c-918c-7ae645b8830d",
   "metadata": {},
   "source": [
    "其中，$P_A$和$P_B$分别是对照组和实验组的观测值。在此案例中$P_A=13\\%$，$P_B=15\\%$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cff4f955-5bff-4651-b413-f4377810cdca",
   "metadata": {},
   "source": [
    "每个组所需的最小样本量为："
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b1c6785-ef73-4498-8143-08d30bc4db00",
   "metadata": {},
   "source": [
    "$$N = \\frac{\\sigma^2}{\\delta^2}(Z_{1-\\frac{\\alpha}{2}}+Z_{1-\\beta})^2 \\\\\n",
    "= (0.13*(1-0.13)+0.15*(1-0.15))/(0.15-0.13)^2*(1.96+0.84)^2   \\\\\n",
    "= 4716$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9a7fbcf1-abd7-4537-a82d-6912341d2a19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4715.760000000005"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(0.13*(1-0.13)+0.15*(1-0.15))/(0.15-0.13)**2*(1.96+0.84)**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a6bb26f5-3efd-44b7-b94c-915930f6b3d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用Python完成最小样本量计算\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "import scipy.stats as stats \n",
    "import statsmodels.stats.api as sms \n",
    "import matplotlib as mpl \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3fe3b955-ef08-4966-b9ac-959bbced5d89",
   "metadata": {},
   "outputs": [],
   "source": [
    "effect_size = sms.proportion_effectsize(0.13, 0.15) # 根据我们的预期比率计算效果量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "553e8565-bfcb-4acf-9dbb-b0fa925ab606",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.0576728617308947"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "effect_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4a6055b7-1634-4190-b8ad-d63d51e0c62a",
   "metadata": {},
   "outputs": [],
   "source": [
    "required_n = sms.NormalIndPower().solve_power( \n",
    "    effect_size,  # 效应量\n",
    "    power=0.8,    # 检验效能\n",
    "    alpha=0.05,   # 显著性水平\n",
    "    ratio=1 ,     # 样本比例\n",
    "    ) # 计算所需的样本量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7ea92938-7bee-4706-bff9-9dbcd89e9179",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4719.4740575998185"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "required_n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5357898a-2b24-440f-8eeb-eabfecef82d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4720.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ceil(required_n) #向上取整"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74156fc7-07de-4ddd-be24-c2c05c458a87",
   "metadata": {},
   "source": [
    "- 实验周期的确定"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5a65ccf-5d0d-4670-b38a-e6a03f0c128a",
   "metadata": {},
   "source": [
    "根据上面最小样本量的计算，我们知道此次AB测试至少需要9440个用户参与测试，假如该落地页以往每天的平均浏览量为1000，则实验周期至少需要的天数为："
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ab113f8-2b67-4a02-bc3c-38fc5a2c46c6",
   "metadata": {},
   "source": [
    "$$试验周期 = 9440/1000 = 9.4 ≈10（天）$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2379c9e9-4a05-4466-b450-a5e63c9d9d62",
   "metadata": {},
   "source": [
    "### **Step4：上线AB测试并收集数据**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1319948-714a-4c5e-b556-de0ac66dfd12",
   "metadata": {},
   "source": [
    "目前市面上大家熟知的大公司基本上都在做AB测试，比如：百度、阿里、腾讯、字节跳动、京东、滴滴、携程、美团等：\n",
    "- 百度：百度统计可视化A/B测试：https://baijiahao.baidu.com/s?id=1735048932168957887&wfr=spider&for=pc\n",
    "- 字节跳动：巨量引擎「AB实验工具」:https://zhuanlan.zhihu.com/p/508366232\n",
    "- 阿里：淘宝推出的 A/B 测试平台，主要针对淘宝商家进行 A/B 测试\n",
    "- 腾讯：腾讯云AB实验平台：https://abtest.qq.com/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d382b39-ccae-4bba-a406-f3dcf2b91b01",
   "metadata": {},
   "source": [
    "大公司做AB测试的主要特点就是。一般都是自研系统。而对于规模不是那么大的企业，也有做AB测试的需求，比如互金、运动。、在线教育、SaaS的都有做A/B测试，但是这部分企业一般是使用第三方的A/B测试工具。目前市面上的第三方AB测试工具主要有：\n",
    "- 神测数据：https://www.sensorsdata.cn/features/ABTesting.html\n",
    "- ABtester：提供web网站A/B测试http://www.abtester.cn/\n",
    "- 热云数据：支持App、web、H5的A/B测试https://www.appadhoc.com/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45b5cc3f-bb25-4d74-9ece-c36fad286920",
   "metadata": {},
   "source": [
    "### Step5：数据分析及假设检验"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ecadddd-8b9e-4005-941b-7877c5c028ac",
   "metadata": {},
   "source": [
    "这里，我们使用的是Kaggle上的A/B测试数据集来模拟练习。（数据集已经给大家下载至网盘，VX加小可爱:littlecat_1205回复【DA】即可领取）"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3be24f25-f0cc-495f-8a74-4087d01cdecc",
   "metadata": {},
   "source": [
    "- **导入数据集**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8e590802-0538-4845-b2c9-04be2b146e53",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入数据集\n",
    "df = pd.read_csv(\"ab_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bbc7d61f-3b50-442d-af50-660aa93b1a90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>group</th>\n",
       "      <th>landing_page</th>\n",
       "      <th>converted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>851104</td>\n",
       "      <td>2017-01-21 22:11:48.556739</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>804228</td>\n",
       "      <td>2017-01-12 08:01:45.159739</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>661590</td>\n",
       "      <td>2017-01-11 16:55:06.154213</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
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       "      <th>3</th>\n",
       "      <td>853541</td>\n",
       "      <td>2017-01-08 18:28:03.143765</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>864975</td>\n",
       "      <td>2017-01-21 01:52:26.210827</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id                   timestamp      group landing_page  converted\n",
       "0   851104  2017-01-21 22:11:48.556739    control     old_page          0\n",
       "1   804228  2017-01-12 08:01:45.159739    control     old_page          0\n",
       "2   661590  2017-01-11 16:55:06.154213  treatment     new_page          0\n",
       "3   853541  2017-01-08 18:28:03.143765  treatment     new_page          0\n",
       "4   864975  2017-01-21 01:52:26.210827    control     old_page          1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4d42fd44-59cf-48ec-b220-67119dad4f91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 294478 entries, 0 to 294477\n",
      "Data columns (total 5 columns):\n",
      " #   Column        Non-Null Count   Dtype \n",
      "---  ------        --------------   ----- \n",
      " 0   user_id       294478 non-null  int64 \n",
      " 1   timestamp     294478 non-null  object\n",
      " 2   group         294478 non-null  object\n",
      " 3   landing_page  294478 non-null  object\n",
      " 4   converted     294478 non-null  int64 \n",
      "dtypes: int64(2), object(3)\n",
      "memory usage: 11.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "258197dd-0a2e-4b31-b0c8-a42adaf1f83d",
   "metadata": {},
   "source": [
    "字段名称含义：\n",
    "- `user_id`：用户ID\n",
    "- `timestamp`：用户访问页面的时间\n",
    "- `group`：用户分组情况（新落地页为treatment组，旧版落地页为control组）\n",
    "- `landing_page`：每位用户看到的落地页（分为新旧两版落地页）\n",
    "- `converted`：是否成功转化（1代表成功转化，0代表未转化）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7b4f99a9-0e08-4745-b038-aacabe611e83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "group\n",
       "treatment    147276\n",
       "control      147202\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"group\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0bb0e6c6-6bc6-415a-ad6b-078f6dabaf98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "landing_page\n",
       "old_page    147239\n",
       "new_page    147239\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"landing_page\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae499566-5131-43f4-af2e-109791bcc572",
   "metadata": {},
   "source": [
    "- **数据清洗**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "45e4647e-cd01-42f2-9c35-326f79486e44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>group</th>\n",
       "      <th>landing_page</th>\n",
       "      <th>converted</th>\n",
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       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>661590</td>\n",
       "      <td>2017-01-11 16:55:06.154213</td>\n",
       "      <td>treatment</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>853541</td>\n",
       "      <td>2017-01-08 18:28:03.143765</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>864975</td>\n",
       "      <td>2017-01-21 01:52:26.210827</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id                   timestamp      group landing_page  converted\n",
       "0   851104  2017-01-21 22:11:48.556739    control     old_page          0\n",
       "1   804228  2017-01-12 08:01:45.159739    control     old_page          0\n",
       "2   661590  2017-01-11 16:55:06.154213  treatment     new_page          0\n",
       "3   853541  2017-01-08 18:28:03.143765  treatment     new_page          0\n",
       "4   864975  2017-01-21 01:52:26.210827    control     old_page          1"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "22ea801b-83c1-4eb2-9929-4063f82ff1fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id         0\n",
       "timestamp       0\n",
       "group           0\n",
       "landing_page    0\n",
       "converted       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 检查缺失值并处理\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "571447af-9243-42b9-9e9e-5004d21432b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 检查重复值并处理\n",
    "df.duplicated().sum() #对于整体数据集没有重复值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c3d75e7e-cb20-4e21-bd4d-9f0d41d9a187",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3894"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 检查用户是否有重复值\n",
    "df[\"user_id\"].duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "46ed2e47-12f8-4cd3-83e6-3b4d998b1b14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2656      698120\n",
       "2893      773192\n",
       "7500      899953\n",
       "8036      790934\n",
       "10218     633793\n",
       "           ...  \n",
       "294308    905197\n",
       "294309    787083\n",
       "294328    641570\n",
       "294331    689637\n",
       "294355    744456\n",
       "Name: user_id, Length: 3894, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"user_id\"].duplicated()][\"user_id\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "448ef37b-3022-4421-a287-5ad7bf063cdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>group</th>\n",
       "      <th>landing_page</th>\n",
       "      <th>converted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3489</th>\n",
       "      <td>899953</td>\n",
       "      <td>2017-01-05 05:36:02.120641</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7500</th>\n",
       "      <td>899953</td>\n",
       "      <td>2017-01-07 03:06:54.068237</td>\n",
       "      <td>control</td>\n",
       "      <td>new_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "      user_id                   timestamp      group landing_page  converted\n",
       "3489   899953  2017-01-05 05:36:02.120641  treatment     new_page          0\n",
       "7500   899953  2017-01-07 03:06:54.068237    control     new_page          0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看其中一位重复用户\n",
    "df[df[\"user_id\"]==899953]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "db23142c-42b4-4539-9f72-63689c41caf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 储存所有的重复用户ID\n",
    "del_id = df[df[\"user_id\"].duplicated()][\"user_id\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "13e45299-96e1-4190-acf4-03f02283532a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3894"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(del_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4c08cf06-7095-4b31-878c-c2a4c4dbd141",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([698120, 773192, 899953, ..., 641570, 689637, 744456], dtype=int64)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "70ce0f05-b8a8-45bb-8d87-35bcf0e6c60a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7788"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"user_id\"].isin(del_id).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5bb14947-d304-4602-a0ca-a00f8fb01c6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         True\n",
       "1         True\n",
       "2         True\n",
       "3         True\n",
       "4         True\n",
       "          ... \n",
       "294473    True\n",
       "294474    True\n",
       "294475    True\n",
       "294476    True\n",
       "294477    True\n",
       "Name: user_id, Length: 294478, dtype: bool"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "~df[\"user_id\"].isin(del_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1595588f-327b-42d8-881f-764385c85d27",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>4</th>\n",
       "      <td>864975</td>\n",
       "      <td>2017-01-21 01:52:26.210827</td>\n",
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       "      <td>751197</td>\n",
       "      <td>2017-01-03 22:28:38.630509</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>294474</th>\n",
       "      <td>945152</td>\n",
       "      <td>2017-01-12 00:51:57.078372</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
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       "      <td>734608</td>\n",
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       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
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       "    <tr>\n",
       "      <th>294476</th>\n",
       "      <td>697314</td>\n",
       "      <td>2017-01-15 01:20:28.957438</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294477</th>\n",
       "      <td>715931</td>\n",
       "      <td>2017-01-16 12:40:24.467417</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>286690 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        user_id                   timestamp      group landing_page  converted\n",
       "0        851104  2017-01-21 22:11:48.556739    control     old_page          0\n",
       "1        804228  2017-01-12 08:01:45.159739    control     old_page          0\n",
       "2        661590  2017-01-11 16:55:06.154213  treatment     new_page          0\n",
       "3        853541  2017-01-08 18:28:03.143765  treatment     new_page          0\n",
       "4        864975  2017-01-21 01:52:26.210827    control     old_page          1\n",
       "...         ...                         ...        ...          ...        ...\n",
       "294473   751197  2017-01-03 22:28:38.630509    control     old_page          0\n",
       "294474   945152  2017-01-12 00:51:57.078372    control     old_page          0\n",
       "294475   734608  2017-01-22 11:45:03.439544    control     old_page          0\n",
       "294476   697314  2017-01-15 01:20:28.957438    control     old_page          0\n",
       "294477   715931  2017-01-16 12:40:24.467417  treatment     new_page          0\n",
       "\n",
       "[286690 rows x 5 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new = df[~df[\"user_id\"].isin(del_id)]\n",
    "df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "cbfe8a17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 286690 entries, 0 to 294477\n",
      "Data columns (total 5 columns):\n",
      " #   Column        Non-Null Count   Dtype         \n",
      "---  ------        --------------   -----         \n",
      " 0   user_id       286690 non-null  int64         \n",
      " 1   timestamp     286690 non-null  datetime64[ns]\n",
      " 2   group         286690 non-null  object        \n",
      " 3   landing_page  286690 non-null  object        \n",
      " 4   converted     286690 non-null  int64         \n",
      "dtypes: datetime64[ns](1), int64(2), object(2)\n",
      "memory usage: 13.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df_new.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d0ce4be",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new['timestamp'] = df_new['timestamp'].str.split(' ').str[0]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "777d5850-b6dd-4b55-8362-6b2e968a08b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.to_datetime(df_new['timestamp'],format='%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4dcdbd04-ab26-42ef-92bb-d18f56a98c59",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 处理时间，并检查时间天数\n",
    "date_days = pd.to_datetime(df_new['timestamp'],format='%Y-%m-%d').dt.strftime('%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "60f4c20d-53d6-48e3-bd34-e95a95912665",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         2017-01-21\n",
       "1         2017-01-12\n",
       "2         2017-01-11\n",
       "3         2017-01-08\n",
       "4         2017-01-21\n",
       "             ...    \n",
       "294473    2017-01-03\n",
       "294474    2017-01-12\n",
       "294475    2017-01-22\n",
       "294476    2017-01-15\n",
       "294477    2017-01-16\n",
       "Name: timestamp, Length: 286690, dtype: object"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date_days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "66f933ac-3c67-4406-974c-95f00a36367d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(date_days.unique()) #有23天"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb2a06cc-0c2f-4a75-9c00-04e11e2f3113",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new['timestamp'].str.split(\" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e095c1c5-659a-456f-a500-cbd4e93d5d17",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "[i[0] for i in df_new['timestamp'].str.split(\" \")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ab78e4f4-13d3-440f-a9f1-7f4d685f7b48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set([i[0] for i in df_new['timestamp'].str.split(\" \")]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "18be23a9-3372-46fd-b295-3c30b254bb1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 286690 entries, 0 to 294477\n",
      "Data columns (total 5 columns):\n",
      " #   Column        Non-Null Count   Dtype         \n",
      "---  ------        --------------   -----         \n",
      " 0   user_id       286690 non-null  int64         \n",
      " 1   timestamp     286690 non-null  datetime64[ns]\n",
      " 2   group         286690 non-null  object        \n",
      " 3   landing_page  286690 non-null  object        \n",
      " 4   converted     286690 non-null  int64         \n",
      "dtypes: datetime64[ns](1), int64(2), object(2)\n",
      "memory usage: 13.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# 确保数据一一匹配\n",
    "df_new.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b0358b06-7b7e-4f9d-a01e-2995643b5ecc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "286690"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# control组对应old_page,treatment组对应new_page\n",
    "(((df_new['group']=='treatment')&(df_new['landing_page']=='new_page'))|((df_new['group']=='control')&(df_new['landing_page']=='old_page'))).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9e477d94-2a6a-4fa5-ac18-d6037e718fb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>landing_page</th>\n",
       "      <th>new_page</th>\n",
       "      <th>old_page</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>control</th>\n",
       "      <td>0</td>\n",
       "      <td>143293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>treatment</th>\n",
       "      <td>143397</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "landing_page  new_page  old_page\n",
       "group                           \n",
       "control              0    143293\n",
       "treatment       143397         0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 确保contorl组每个用户看到的是旧页面，treatment组看到的是新页面\n",
    "pd.crosstab(df_new['group'], df_new['landing_page'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c627858-5adb-45f3-a127-d88f21c98f7c",
   "metadata": {},
   "source": [
    "至此，所有的数据清洗工作已完成。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7b6f595-ba6c-4783-8be7-a466fac02da1",
   "metadata": {},
   "source": [
    "- **抽样**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56ccd67a-ffe0-4a9e-9b0a-83b86c35c2a2",
   "metadata": {},
   "source": [
    "根据前面最小样本量的计算，我们至少需要每组4720个样本，这里我们选择每组抽样5000个（实际工作中不需要抽样这一步）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "dfc494f8-85bf-486a-8026-b39e761b1041",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>group</th>\n",
       "      <th>landing_page</th>\n",
       "      <th>converted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>186628</th>\n",
       "      <td>763854</td>\n",
       "      <td>2017-01-21</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57904</th>\n",
       "      <td>690555</td>\n",
       "      <td>2017-01-18</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13276</th>\n",
       "      <td>861520</td>\n",
       "      <td>2017-01-06</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228415</th>\n",
       "      <td>630778</td>\n",
       "      <td>2017-01-05</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96065</th>\n",
       "      <td>656634</td>\n",
       "      <td>2017-01-04</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185119</th>\n",
       "      <td>665427</td>\n",
       "      <td>2017-01-18</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232747</th>\n",
       "      <td>763015</td>\n",
       "      <td>2017-01-13</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274312</th>\n",
       "      <td>945994</td>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173894</th>\n",
       "      <td>692742</td>\n",
       "      <td>2017-01-11</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76979</th>\n",
       "      <td>662240</td>\n",
       "      <td>2017-01-14</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        user_id  timestamp    group landing_page  converted\n",
       "186628   763854 2017-01-21  control     old_page          0\n",
       "57904    690555 2017-01-18  control     old_page          0\n",
       "13276    861520 2017-01-06  control     old_page          0\n",
       "228415   630778 2017-01-05  control     old_page          0\n",
       "96065    656634 2017-01-04  control     old_page          0\n",
       "...         ...        ...      ...          ...        ...\n",
       "185119   665427 2017-01-18  control     old_page          0\n",
       "232747   763015 2017-01-13  control     old_page          0\n",
       "274312   945994 2017-01-03  control     old_page          0\n",
       "173894   692742 2017-01-11  control     old_page          1\n",
       "76979    662240 2017-01-14  control     old_page          0\n",
       "\n",
       "[5000 rows x 5 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new[df_new['group'] == 'control'].sample(n=required_n, random_state=22)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "353c220b-d0ef-4f7a-ab7e-b9b09d9e28eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>group</th>\n",
       "      <th>landing_page</th>\n",
       "      <th>converted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>763854</td>\n",
       "      <td>2017-01-21</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>690555</td>\n",
       "      <td>2017-01-18</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>861520</td>\n",
       "      <td>2017-01-06</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>630778</td>\n",
       "      <td>2017-01-05</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>656634</td>\n",
       "      <td>2017-01-04</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>787786</td>\n",
       "      <td>2017-01-06</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>770196</td>\n",
       "      <td>2017-01-24</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>775724</td>\n",
       "      <td>2017-01-18</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>920254</td>\n",
       "      <td>2017-01-23</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>823865</td>\n",
       "      <td>2017-01-21</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  timestamp      group landing_page  converted\n",
       "0      763854 2017-01-21    control     old_page          0\n",
       "1      690555 2017-01-18    control     old_page          0\n",
       "2      861520 2017-01-06    control     old_page          0\n",
       "3      630778 2017-01-05    control     old_page          0\n",
       "4      656634 2017-01-04    control     old_page          0\n",
       "...       ...        ...        ...          ...        ...\n",
       "9995   787786 2017-01-06  treatment     new_page          0\n",
       "9996   770196 2017-01-24  treatment     new_page          0\n",
       "9997   775724 2017-01-18  treatment     new_page          0\n",
       "9998   920254 2017-01-23  treatment     new_page          0\n",
       "9999   823865 2017-01-21  treatment     new_page          0\n",
       "\n",
       "[10000 rows x 5 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "required_n = 5000\n",
    "control_sample = df_new[df_new['group'] == 'control'].sample(n=required_n, random_state=22) \n",
    "treatment_sample = df_new[df_new['group'] == 'treatment'].sample(n=required_n, random_state=22) \n",
    "\n",
    "ab_test = pd.concat([control_sample, treatment_sample], axis=0) \n",
    "ab_test.reset_index(drop=True, inplace=True)\n",
    "ab_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "cb42eb4d-3b94-487b-a0cb-bed578462068",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>landing_page</th>\n",
       "      <th>new_page</th>\n",
       "      <th>old_page</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>control</th>\n",
       "      <td>0</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>treatment</th>\n",
       "      <td>5000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "landing_page  new_page  old_page\n",
       "group                           \n",
       "control              0      5000\n",
       "treatment         5000         0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(ab_test['group'], ab_test['landing_page'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69b9032e-7c8e-414c-bb6d-4ebd83e820cb",
   "metadata": {},
   "source": [
    "- **可视化结果【选做】**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "2db1d9a1-2725-4cd7-84b1-76013c8f35c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>group</th>\n",
       "      <th>landing_page</th>\n",
       "      <th>converted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>763854</td>\n",
       "      <td>2017-01-21</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>690555</td>\n",
       "      <td>2017-01-18</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>861520</td>\n",
       "      <td>2017-01-06</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>630778</td>\n",
       "      <td>2017-01-05</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>656634</td>\n",
       "      <td>2017-01-04</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  timestamp    group landing_page  converted\n",
       "0   763854 2017-01-21  control     old_page          0\n",
       "1   690555 2017-01-18  control     old_page          0\n",
       "2   861520 2017-01-06  control     old_page          0\n",
       "3   630778 2017-01-05  control     old_page          0\n",
       "4   656634 2017-01-04  control     old_page          0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ab_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "2fbff8d5-1c26-465a-ae92-10e2c91968fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\87205\\AppData\\Local\\Temp\\ipykernel_25124\\3751263133.py:1: FutureWarning: The provided callable <function mean at 0x000002BD04419B40> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"mean\" instead.\n",
      "  conversion_rates = ab_test.groupby('group')['converted'].agg([np.mean,np.std])\n",
      "C:\\Users\\87205\\AppData\\Local\\Temp\\ipykernel_25124\\3751263133.py:1: FutureWarning: The provided callable <function std at 0x000002BD04419C60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"std\" instead.\n",
      "  conversion_rates = ab_test.groupby('group')['converted'].agg([np.mean,np.std])\n"
     ]
    }
   ],
   "source": [
    "conversion_rates = ab_test.groupby('group')['converted'].agg([np.mean,np.std])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "6871f894-3615-4256-bb4d-46502e11a238",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>control</th>\n",
       "      <td>0.1232</td>\n",
       "      <td>0.328700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>treatment</th>\n",
       "      <td>0.1266</td>\n",
       "      <td>0.332558</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             mean       std\n",
       "group                      \n",
       "control    0.1232  0.328700\n",
       "treatment  0.1266  0.332558"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conversion_rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d5f5dd96-6b7a-4b14-9f43-365ff9361233",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\87205\\AppData\\Local\\Temp\\ipykernel_25124\\2556401037.py:1: FutureWarning: The provided callable <function mean at 0x000002BD04419B40> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"mean\" instead.\n",
      "  conversion_rates = ab_test.groupby('group')['converted'].agg([np.mean,np.std])\n",
      "C:\\Users\\87205\\AppData\\Local\\Temp\\ipykernel_25124\\2556401037.py:1: FutureWarning: The provided callable <function std at 0x000002BD04419C60> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"std\" instead.\n",
      "  conversion_rates = ab_test.groupby('group')['converted'].agg([np.mean,np.std])\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "      <th></th>\n",
       "      <th>conversion_rate</th>\n",
       "      <th>std_deviation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>control</th>\n",
       "      <td>0.1232</td>\n",
       "      <td>0.328700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>treatment</th>\n",
       "      <td>0.1266</td>\n",
       "      <td>0.332558</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           conversion_rate  std_deviation\n",
       "group                                    \n",
       "control             0.1232       0.328700\n",
       "treatment           0.1266       0.332558"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conversion_rates = ab_test.groupby('group')['converted'].agg([np.mean,np.std])\n",
    "\n",
    "conversion_rates.columns = ['conversion_rate', 'std_deviation']\n",
    "conversion_rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "8cc0719b-b2c1-4757-9742-aff5b3231dda",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\87205\\AppData\\Local\\Temp\\ipykernel_25124\\2788269419.py:7: FutureWarning: \n",
      "\n",
      "The `ci` parameter is deprecated. Use `errorbar=('ci', False)` for the same effect.\n",
      "\n",
      "  sns.barplot(x=ab_test['group'], y=ab_test['converted'], ci=False)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['font.family'] = 'SimHei'\n",
    "# plt.rcParams['font.family'] = \"Arial Unicode MS\"\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "plt.figure(figsize=(8,6),dpi=60) \n",
    "\n",
    "sns.barplot(x=ab_test['group'], y=ab_test['converted'], ci=False) \n",
    "\n",
    "plt.ylim(0, 0.17) \n",
    "plt.title( '分组转化率', fontsize=20) \n",
    "plt.xlabel('分组', fontsize=15) \n",
    "plt.ylabel('转化(比例)', fontsize=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d35f8d87-f388-406f-a081-58a3009e5407",
   "metadata": {},
   "source": [
    "从上面的统计数据来看，新旧两版落地页的表现结果非常相近，相比于旧版落地页，新版落地页的转化率略微好一点点，高了0.3%<br><br>\n",
    "那么，这种差异在统计学上显著么？我们可以直接说，新版落地页更好么？"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edb613e5-4a40-4283-96ed-67c50515c3e8",
   "metadata": {},
   "source": [
    "- **假设检验**"
   ]
  },
  {
   "attachments": {
    "113b10d7-d6f7-43a9-a80a-2a6cd0e67015.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "79bc53ac-3bee-4330-acfe-95eff9d514ec",
   "metadata": {},
   "source": [
    "我们分析的最后一步就是假设检验了。那么，具体选择哪一种假设检验呢？在统计学中，当样本容量较大时（一般是大于30），我们可以使用Z检验或者t检验。<br><br>\n",
    "![image.png](attachment:113b10d7-d6f7-43a9-a80a-2a6cd0e67015.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7646ec7-5bb6-4fe6-9677-4882ca480f7d",
   "metadata": {},
   "source": [
    "在这个案例中，由于我们的样本非常大，所以我们使用Z检验。Python中的statsmodels.stats.proportion模块可以来计算P值和置信区间："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "b70955e7-c582-4572-bee3-6a0ee2917f9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.stats.proportion import proportions_ztest, proportion_confint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "2bcf0442-9bac-4d4f-bf56-4767c651387c",
   "metadata": {},
   "outputs": [],
   "source": [
    "control_results = ab_test[ab_test['group'] == 'control']['converted']\n",
    "treatment_results = ab_test[ab_test['group'] == 'treatment']['converted']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "23d0be06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "z statistic: -0.51\n",
      "p-value: 0.607\n",
      "ci 95% for control group: [0.114, 0.132]\n",
      "ci 95% for treatment group: [0.117, 0.136]\n"
     ]
    }
   ],
   "source": [
    "n_con = control_results.count()\n",
    "n_treat = treatment_results.count()\n",
    "successes = [control_results.sum(), treatment_results.sum()]\n",
    "nobs = [n_con, n_treat]\n",
    "\n",
    "z_stat, pval = proportions_ztest(successes, nobs=nobs)\n",
    "(lower_con, lower_treat), (upper_con, upper_treat) = proportion_confint(successes, nobs=nobs, alpha=0.05)\n",
    "\n",
    "print(f'z statistic: {z_stat:.2f}')\n",
    "print(f'p-value: {pval:.3f}')\n",
    "print(f'ci 95% for control group: [{lower_con:.3f}, {upper_con:.3f}]')\n",
    "print(f'ci 95% for treatment group: [{lower_treat:.3f}, {upper_treat:.3f}]')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "eded45eb-75c6-4110-80d8-7c8793dcf8c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0\n",
       "1       0\n",
       "2       0\n",
       "3       0\n",
       "4       0\n",
       "       ..\n",
       "4995    0\n",
       "4996    0\n",
       "4997    0\n",
       "4998    1\n",
       "4999    0\n",
       "Name: converted, Length: 5000, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "control_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "fa9df00a-4af8-46dc-aef7-412915a3f8be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5000    0\n",
       "5001    0\n",
       "5002    0\n",
       "5003    0\n",
       "5004    0\n",
       "       ..\n",
       "9995    0\n",
       "9996    0\n",
       "9997    0\n",
       "9998    0\n",
       "9999    0\n",
       "Name: converted, Length: 5000, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "treatment_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "abec689e-0a9f-4a2b-b3db-5bb01eec0055",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "616"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "control_results.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "7076c65c-3323-4a30-8e27-9077e5e5d2d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_con = control_results.count()\n",
    "n_treat = treatment_results.count()\n",
    "successes = [control_results.sum(), treatment_results.sum()]\n",
    "nobs = [n_con, n_treat]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "257856e2-2898-4048-8a26-bd30adc00b59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5000, 5000]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nobs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "2b362075-378a-45fe-acdd-821f59c58f83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[616, 633]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "successes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "f1a0d737-d76a-4416-8da7-87032cdee399",
   "metadata": {},
   "outputs": [],
   "source": [
    "z_stat, pval = proportions_ztest(successes, nobs=nobs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "628dc657-fa0b-49ab-8087-709d00243485",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.5142080369388495"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z_stat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "2762cd2e-e2d9-4a6a-a95b-665aa0434241",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6071065493288474"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "06c2f525-e799-495a-9db4-f18532849ebb",
   "metadata": {},
   "outputs": [],
   "source": [
    "(lower_con, lower_treat), (upper_con, upper_treat) = proportion_confint(successes, nobs=nobs, alpha=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "91913848-4db3-4b5d-87cb-d4e34e2ab7d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "z statistic: -0.51\n",
      "p-value: 0.607\n",
      "ci 95% for control group: [0.114, 0.132]\n",
      "ci 95% for treatment group: [0.117, 0.136]\n"
     ]
    }
   ],
   "source": [
    "print(f'z statistic: {z_stat:.2f}')\n",
    "print(f'p-value: {pval:.3f}')\n",
    "print(f'ci 95% for control group: [{lower_con:.3f}, {upper_con:.3f}]')\n",
    "print(f'ci 95% for treatment group: [{lower_treat:.3f}, {upper_treat:.3f}]')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ac93a16-2fa7-4bbd-9da3-0efede1f26a0",
   "metadata": {},
   "source": [
    "### Step6：分析结果及建议"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adb08f08-66ec-41b5-ab7a-b04d6cf3cbc5",
   "metadata": {},
   "source": [
    "由于我们计算出来的P值=0.607远高于显著水平$\\alpha=0.05$，所以我们不能拒绝原假设H0.这意味着新版落地页与旧版落地页没有明显不同（更不用说更好了……）"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2c5394e-146a-415f-98ec-b4ad6a54916f",
   "metadata": {},
   "source": [
    "此外，我们继续看置信区间，treatment组的置信区间为$[0.117,0.136]$，可以看出：\n",
    "- 它包括我们的转化率基准线13%\n",
    "- 它不包括我们的转化率目标值15%"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3c02cf0-8fbb-4165-8d96-5767da0f7c8a",
   "metadata": {},
   "source": [
    "也可以说明，新版落地页的真实转化率更有可能与我们的基线相似，而没有办法达到我们期望的15%。进一步证明了，新版设计并不是一个很好的改进。"
   ]
  }
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